With AI spending soaring and shifting preferences, US healthcare organisations grapple with choosing between internal development and vendor solutions to enhance patient care and streamline operations amid regulatory and talent challenges.
In 2024, the United States healthcare sector is navigating a pivotal moment in the adoption of generative artificial intelligence (AI) technologies, aiming to enhance patient care, streamline administrative processes, and alleviate bu...
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Healthcare providers face a strategic crossroads: whether to develop AI tools internally or to procure ready-made solutions from third-party vendors. This decision hinges on considerations such as cost, customization needs, integration capability with existing systems, ongoing support, and the critical regulatory environment governing patient data privacy. Historically slow to adopt new technologies due to regulatory scrutiny and the intricacies of clinical workflows, the industry’s current rapid embrace of AI signals a transformative period.
Building AI tools in-house, chosen by around 47% of healthcare organisations in 2024, offers several advantages. It allows for precise customization to specific workflows and medical domains, ensuring AI solutions adapt to local regulations and clinical practices. Internal development also facilitates tighter integration with legacy and current electronic health record (EHR) systems and financial software, enhancing operational coherence. Moreover, developing AI internally addresses major data security concerns by limiting exposure to external parties and easing compliance with laws like HIPAA. While the upfront costs and demand for expert talent can be significant barriers, 26% of AI projects reportedly fail due to budgetary issues, building internally can reduce long-term dependencies on vendors and ongoing cost escalations.
However, these benefits come with challenges. The scarcity and high cost of AI professionals with healthcare expertise can strain resources. Maintaining and scaling AI systems demands dedicated IT staff, which many organisations lack. Additionally, internal development often translates into longer deployment timelines, potentially delaying operational improvements.
On the opposite spectrum, 53% of healthcare groups opted to purchase AI tools in 2024, attracted by vendors offering ready-to-deploy or easily customizable AI products. These tools have the advantage of faster implementation, often backed by case studies demonstrating return on investment and operational efficacy. Companies like Eleos Health have developed AI that automates clinical note-taking and integrates with major EHRs, saving physicians valuable time. Vendor solutions also come with the benefit of ongoing updates and expert regulatory compliance baked into their design, alleviating some pressures on hospital compliance teams.
Nonetheless, relying on third-party AI brings trade-offs, including limited customizability which can lead to workflow friction. There is also the risk of vendor lock-in, where hospitals might face increased costs or degraded service over time with limited exit options. Data privacy concerns arise from transferring sensitive patient information to outside providers and often storing it in the cloud. Moreover, despite vendors’ assurances, integrating third-party AI with existing hospital IT systems frequently requires substantial additional effort and expertise.
AI-driven workflow automation remains central to the build-versus-buy debate. Healthcare organisations employ AI to tackle some of the most resource-intensive tasks such as documentation, patient registration, and revenue cycle management. Many use retrieval-augmented generation (RAG) systems to synthesise vast clinical data, while a smaller but growing number experiment with agentic AI capable of independently performing complex multi-step tasks. Success in these areas depends heavily on AI systems’ integration capacity, adaptability to clinical specialties, robust security controls, and user-friendly design.
Looking ahead, U.S. healthcare faces unique challenges when adopting AI. Regulatory frameworks like HIPAA and HITECH impose rigorous data governance requirements. Budget constraints often force organisations to demonstrate clear financial benefits for AI investments. The heterogeneity of the healthcare ecosystem, from single-provider clinics to sprawling hospital systems, increases the need for flexible AI solutions. Additionally, persistent talent shortages tilt some organisations toward vendor solutions despite their limitations.
Recent market dynamics underscore a shift: whereas in 2023, about 80% of healthcare entities purchased AI from vendors, that figure dropped to 53% in 2024, highlighting growing interest in internal AI development. The rise of agentic AI, which can handle tasks with increasing autonomy such as patient monitoring or complex decision support, may further influence this trend in the near future.
Supporting this momentum, healthcare startups like Suki have attracted significant investment, in October 2024, Suki raised $70 million to enhance its AI assistant platforms that reduce administrative burdens and integrate seamlessly with major EHR systems such as Epic and Cerner. These developments highlight the critical role of AI not just as a clinical tool but as a workflow optimiser.
Moreover, addressing the sector’s AI talent gap, initiatives like the AI credential program launched in partnership between Adtalem Global Education and Google Cloud will equip healthcare professionals with practical skills needed to implement AI safely and effectively by 2026. Programs combining clinical AI applications, ethics, and patient safety aim to prepare the workforce for this digital transformation amid ongoing staffing challenges.
AI adoption in healthcare is also outpacing other industries by a substantial margin. A 2025 report notes that healthcare organisations adopt AI 2.2 times faster than other sectors, with spending reaching $1.4 billion, nearly tripling from the previous year. More than a fifth of healthcare providers now deploy domain-specific AI, with health systems leading adoption rates.
Globally, the AI in healthcare market is poised for expansive growth. Projections estimate the U.S. AI healthcare market will balloon from $8.41 billion in 2024 to over $195 billion by 2034, driven by rapid innovation and increasing integration of AI in clinical and administrative functions. Parallel growth in Asia-Pacific, particularly China and India, highlights the global scale of this transformation.
Meanwhile, the European Union is advancing its own AI strategy focused on strategic autonomy, investing over €1 billion to promote AI deployment in healthcare and other critical sectors under strict regulatory oversight. This underscores the international race to harness AI’s transformative potential while balancing security and ethical considerations.
For healthcare managers, clinic owners, and IT decision-makers in the U.S., the choice between building AI tools internally or procuring them externally remains nuanced. It requires balancing customisability, integration capabilities, regulatory compliance, total cost of ownership, and available expertise. Ultimately, the goal is to harness AI’s power to improve patient outcomes, reduce clinician burnout, and streamline operations, whether through bespoke development or leveraging vendor innovations in this rapidly evolving landscape.
Source: Noah Wire Services
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